MICROSOFT AZURE MACHINE LEARNING. Oscar Naim Microsoft

MICROSOFT AZURE MACHINE LEARNING Oscar Naim Microsoft

Microsoft Azure Machine Learning

What is Machine Learning? Azure Machine Learning: How it works Azure Machine Learning in action Get started Contents

Delivering on one of the old dreams of Microsoft co-founder Bill Gates: Computers that can see, hear and understand. John Platt Distinguished scientist at Microsoft Research What is Machine Learning? Predictive computing systems become smarter with experience

Why Learn? Delivering on one of the old dreams of Microsoft co-founder Bill Gates: Computers that can see, hear and understand. John Platt Distinguished scientist at Microsoft Research Learn it when you can t code it (e.g. speech recognition) Learn it when you can t scale it (e.g. recommendations) Learn it when you have to adapt/personalize (e.g. predictive typing) Learn it when you can t track it (e.g. robot control)

The United States Postal Service processed over 150 billion pieces of mail in 2013 far too much for efficient human sorting. But as recently as 1997, only 10% of hand-addressed mail was successfully sorted automatically.

The challenge in automation is enabling computers to interpret endless variation in handwriting.

By providing feedback, the Postal Service was able to train computers to accurately read human handwriting. Today, with the help of machine learning, over 98% of all mail is successfully processed by machines.

Microsoft & Machine Learning 15 years of realizing innovation 1999 2004 2005 2008 2010 2012 2014 Computers work on users behalf, filtering junk email Microsoft search engine built with machine learning SQL Server enables data mining Bing Maps ships with ML trafficprediction service Microsoft Kinect can watch users gestures Successful, real-time, speech-tospeech translation Microsoft launches Azure Machine Learning John Platt, Distinguished scientist at Microsoft Research Machine learning is pervasive throughout Microsoft products.

Expensive Siloed data Fragmented tools Deployment complexity Huge set-up costs of tools, expertise, and compute/storage capacity create unnecessary barriers to entry Siloed and cumbersome data management restricts access to data Complex and fragmented tools limit participation in exploring data and building models Many models never achieve business value due to difficulties with deploying to production Break away from industry limitations

Azure Machine Learning offers a data science experience that is directly accessible to business analysts and domain experts, reducing complexity and broadening participation through better tooling. Hans Kristiansen Capgemini Azure Machine Learning How it works Enable custom predictive analytics solutions at the speed of the market

The Environments Azure Portal The Team Azure Ops Team ML Studio Data Scientists ML API service Developers

Web Apps Mobile Apps PowerBI/Dashboards ML API service Developer Azure Portal & ML API service Azure Ops Team ML Studio Data Scientist HDInsight Azure Storage Desktop Data

Web Apps Mobile Apps PowerBI/Dashboards Business users easily access results: from anywhere, on any device ML API service ML API service and the Developer Developer Tested models available as an url that can be called from any end point Azure Portal & and the Azure Ops Team ML API service Azure Portal & ML API service Create ML Studio workspace Assign storage account(s) Monitor ML consumption See alerts when model is ready Azure Ops Team Deploy models to web service ML Studio and the Data Scientist Access and prepare data Create, test and train models Collaborate One click to stage for production via the API service Data Scientist HDInsight Azure Storage Desktop Data

Fully managed Easy to use Tested solutions Deploy in minutes No software to install, no hardware to manage, and one portal to view and update Simple drag, drop and connect interface you can access and share from anywhere Access to sample experiments, tested algorithms, support for custom R, and over 350 R packages Tooled for quick deployment, hand-off and updates

There was zero percent chance we were going to take a step backwards and consider a machine learning solution that wasn t well-established and proven effective in the cloud. Azure Machine Learning in action Real world examples Kristian Kimbro Rickard MAX451

Smart Buildings The Center for Building Performance and Diagnostics uses weather forecasts, real-time temperature reads, and behavioral research data to optimize building heating and cooling systems in real-time. Key Benefits User friendly set up and integration with existing systems Seamless data handling Accessible and easy to use across backgrounds Quickly compare algorithms The ease of implementation makes machine learning accessible to a larger number of investigators with various backgrounds even non-data scientists. Bertrand Lasternas Carnegie Mellon

Demand Forecasting Pier 1 partnered with MAX451 to delight loyalty customers by using historical and behavioral data to predict what products they want next. Key Benefits Ease of use across skillsets Fast time to meaningful results Accessible via the cloud We are especially pleased that our analysts can focus on the results and not worry about the complex algorithms behind the scenes. Andrew Laudato Pier 1 Imports

Investment Optimization Icertis, a cloud solutions provider, built a predictive model using past performance data to determine the optimal locations for its clients to build new retail stores. Key Benefits Quickly build, test and verify models No new scripting or coding languages Easily import and modify algorithms developed outside the solution The standout benefit for us was to quickly build and test predictive models and verify their results. There is no cognitive overhead to learn new scripting or coding language. Yogesh Dandawate Icertis Applied Cloud

Churn analysis Ad targeting Image detection & classification Imagine what machine learning could do for your business. Equipment monitoring Recommendations Forecasting Spam filtering Fraud detection Anomaly detection

Microsoft has a solid track record for creating user-friendly tools, and Pier 1 is helping prove Microsoft can take something as complex as machine learning and make it accessible via the cloud Learn more and sign up for a free trial of Azure azure.com/ml Andy Laudato Pier 1 Imports